1. Field of Invention
The present invention relates to a method, a system and a computer program product for object color correction, and more particularly to an object color correction method, system, and computer program product that perform color correction on the same subject matter in different images with different color levels.
2. Related Art
Currently, for traffic safety or public security, monitors are disposed at crossings or exits and entrances of buildings. However, due to the influence of angles at which the monitors are disposed and light, the phenomenon of color distortion occurs easily in display on screens.
Most of existing methods for increasing the resolution are implemented with hardware. A current super-resolution image reconstruction technology is classified into two categories, namely, static image super-resolution and dynamic image super-resolution. The static image super-resolution refers to reconstructing a high resolution image with contents of a single low resolution image. Methods currently commonly used for the static image super-resolution can be classified into three categories: 1. polynomial interpolation; 2. edge-directed interpolation; and 3. sample-based super-resolution technology.
Firstly, the polynomial interpolation is currently the most widely applied method for the static image super-resolution, and mainly has such advantages as a simple algorithm, a high operation speed, and a good effect in smooth regions. Common polynomial interpolation methods are zero-order interpolation, bilinear interpolation, and bicubic interpolation.
Secondly, the edge-directed interpolation method is proposed mainly for solving the problem that high-frequency information cannot be effectively presented with the polynomial interpolation. The high-frequency information refers to texture and edge regions of an image. Because sensing of human eyes for the texture region is weak, the emphasis of the method is to reserve an edge part. The basic idea of the algorithm is to firstly find parts belonging to edges in the image, and determine directions of these edges, and then through the two kinds of edge-relevant information, perform a suitable conversion with a sampling function in a direction of an edge that is detected, so that edges in different directions each have one sampling function in itself direction to perform interpolation. Since the practice takes not only characteristics of the entire image but also characteristics of each region in the image into consideration, the effect of image amplification is good. However, the method can only perform interpolation and reconstruction to reserve image edges by finding trends of the edges, and thus can be achieved only through rather complex operation. Furthermore, the method cannot be used in a high-frequency texture region, so that there is still room for improvement in terms of the visual quality.
Thirdly, the concept of the sample-based super-resolution is to use an existing high resolution image to serve as training data of a model, and to generate a database of possible corresponding relationships between a low resolution and a high resolution, thereby enhancing high-frequency information that a low resolution image lacks. The method improves the visual quality of the image mainly with emulated high-frequency information rather than original real high-frequency information of the image. The problems existing in the method are how to establish a correspondence database of high frequency and low frequency that can meet all demands, and how to quickly find matched data from the huge correspondence database.